5 papers · 1 filter
Neural Entropic Optimal Transport and Gromov-Wasserstein Alignment
Tao Wang, Ziv Goldfeld
Optimal transport (OT) and Gromov-Wasserstein (GW) alignment are powerful frameworks for geometrically driven matching of probability distributions, yet their large-scale usage is…
Robust Alignment via Partial Gromov-Wasserstein Distances
Xiaoyun Gong, Sloan Nietert, Ziv Goldfeld
The Gromov-Wasserstein (GW) problem provides a powerful framework for aligning heterogeneous datasets by matching their internal structures in a way that minimizes distortion. Howe…
Limit Laws for Gromov-Wasserstein Alignment with Applications to Testing Graph Isomorphisms
Gabriel Rioux, Ziv Goldfeld, Kengo Kato
The Gromov-Wasserstein (GW) distance enables comparing metric measure spaces based solely on their internal structure, making it invariant to isomorphic transformations. This prope…
Stability and statistical inference for semidiscrete optimal transport maps
Ritwik Sadhu, Ziv Goldfeld, Kengo Kato
We study statistical inference for the optimal transport (OT) map (also known as the Brenier map) from a known absolutely continuous reference distribution onto an unknown finitely…
Neural Estimation Of Entropic Optimal Transport
Tao Wang, Ziv Goldfeld
Optimal transport (OT) serves as a natural framework for comparing probability measures, with applications in statistics, machine learning, and applied mathematics. Alas, statistic…